Software Engineer

Google Fiber
November 2014 -- February 2017, New York City

Lead engineering of Fiber-managed WiFi guest networks for apartment
buildings and small businesses. Adapted autoprovisioning software to go from
idea to pilot in three months. Handled communication with product management,
sales, and property managers in the pilot deployment.

Develop autoprovisioning software that allows newly powered up TV boxes to
learn their own WiFi configuration and start playing TV immediately.

Secure autoprovisioning and guest networks so customers can use them safely,
with minijail to contain server processes and tc to let
roaming TV boxes drop unsolicited traffic cheaply.

Adapt the isostream network measurement tool to simulate
high-definition wireless TV streams. Deployed this simulation to Fiber TV
subscribers, found sufficient bandwidth for wireless TV and critical WiFi
driver bugs. After fixes shipped, used simulation to verify them in the field.

Run an ongoing dogfood for the Google Fiber wifi router in 20%
time. Set up 300 tech and business Googlers with routers, moderated a mailing
list to stay in touch with them, and investigated and resolved issues that
came up. Automated orders so I could do this part time; without automation
running a dogfood this size is a full-time job.

20% projects, July 2014 -- November 2014

Extend our in-house logs processing utility Turbogrinder (similar in spirit
to Stackdriver logs-based metrics, but simple enough
for one engineer to build and run) to read logs published to our QA server.
This let us test it more quickly, and without using sensitive data access.

Build distribution support for Turbogrinder, allowing it to summarize time
series information including device temperature and ping round-trip times from
devices in the field.

Technical Solutions Engineer

Google Cloud Search
July 2013 -- November 2014, New York City

Remotely diagnose and repair malfunctioning Google Search Appliances.

Write customer-deployable support scripts to troubleshoot Appliances in
embedded applications where no network access is available.

Develop a customer-deployable configuration profile to quiet fan operation on
Search Appliances, resolving several dozen escalated cases that had been
previously thought infeasible. This was one of the biggest issues at the time;
I got two peer bonuses and a spot bonus for this work.

Automate support for customers with common problems by extending our team's
support AI to handle Search Appliance cases.

Work with external partners and vendors to ensure successful deployments at
large government and commercial customers.

Consultant

Booz Allen Hamilton
June 2008 -- July 2013, McLean, VA

Developed custom mapping software in Python to ingest and visualize months of
NAIS vessel movement data for a US Government client. Reduced the
time it takes to go from raw input to finished maps from a week to a day.

Built a LiDAR data warehouse for a US Government client. Developed a Python
Web application using Django, C++ data processing utilities, Celery to
schedule runs of these utilities, and an Oracle Spatial backend to store raw
and processed data.